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EchoLux

Financiado

Intelligent Optoacoustic Radiomics via Synergistic Integration of System Models...

Intelligent Optoacoustic Radiomics via Synergistic Integration of System Models and Medical Knowledge Radiomics - the extraction of clinically relevant information from complex medical imaging data via mathematics and data science - is on the verge of becoming a main player in clinical research and medicine. However, the current... ver más

30/06/2027

HMGU

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

HELMHOLTZ ZENTRUM MUENCHEN DEUTSCHES FORSCHUN... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2022-02-22

ERC-2021-STG: ERC STARTING GRANTS Scope:Objectives

I+D

Cerrada hace 3 años

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

HMGU

1.50M€ | Lider

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Duración del proyecto: 64 meses Fecha Inicio: 2022-02-22
Fecha Fin: 2027-06-30

Líder del proyecto

HELMHOLTZ ZENTRUM MUENCHEN DEUTSCHES FORSCHUN... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 1M€

Descripción del proyecto Radiomics - the extraction of clinically relevant information from complex medical imaging data via mathematics and data science - is on the verge of becoming a main player in clinical research and medicine. However, the current radiomics workflow mostly relies on feature engineering and black box machine learning, lagging behind the state-of-the-art in explainable artificial intelligence. I will exploit my broad expertise in mathematics, informatics, and biomedical imaging to implement intelligent radiomics by integrating the whole imaging value chain - ranging from imaging hardware, over image formation, to medical interpretation of the data - into an intelligent software environment. EchoLux is a paradigm shift from black box machine learning to truly intelligent radiomics. EchoLux will be realized in three steps: 1) Modelling the imaging system via a digital twin approach using dedicated physical phantom data and a reference dataset acquired from healthy volunteers; 2) Development and integration of a medical knowledge base that captures the effects of disease on the imaged tissue; 3) Integration of system and medical tissue models into a Bayesian reasoning framework that is able to perform explainable diagnostics and medical knowledge discovery. The clinical use case is optoacoustic imaging of peripheral neuropathy. I recently demonstrated that the internal structure and vascular supply of peripheral nerves can be visualized in unprecedented detail by exploiting the molecular contrast of optoacoustic imaging. The EchoLux approach, thus, has the potential to enable early detection of pathological changes in peripheral nerves, e.g., in conjunction with diabetes. EchoLux links the clinical imaging data simultaneously back to the hardware and forward to the medical decision making. This holistic and interdisciplinary approach will leverage radiomics in general and boost the clinical value of optoacoustic imaging.

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